Cardiac signal qt interval detection

ABSTRACT

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

This application claims the benefit of U.S. Provisional PatentApplication No. 63/004,017, filed Apr. 2, 2020, the entire content ofwhich is incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to cardiac monitoring and, more particularly, todetection of QT intervals or corrected QT intervals (QTc) in cardiacsignals.

BACKGROUND

Cardiac signal analysis may be performed by a variety of devices, suchas implantable medical devices (IMDs), insertable cardiac monitors(ICMs) and external devices (e.g., smart watches, fitness monitors,mobile devices, Holter monitors, wearable defibrillators, or the like).For example, devices may be configured to process cardiac signals (e.g.,cardiac electrograms (EGMs) and electrocardiograms (ECGs)) sensed by oneor more electrodes. Features of cardiac signals may include the P-wave,Q-wave, R-wave, S-wave, QRS-complex, and T-wave. A QT interval is thetime from the beginning of the QRS complex to the end of the T-wave. AQTc interval is a QT interval that has been normalized or corrected withrespect to a heart rate using a formula. Accurate detection anddelineation of features in cardiac signals, such as QT intervals or QTcintervals, may be of importance for monitoring patient health, such asrisk of sudden cardiac death.

SUMMARY

In general, this disclosure is directed to devices and techniques foridentifying one or more features and/or determining one or moreparameters of a cardiac signal (e.g., EGM and/or ECG) of a patient. Forexample, the disclosure describes techniques for identifying a QTinterval or QTc interval, which may enable predicting whether a patientis experiencing or will experience a tachyarrhythmia or other abnormalcardiac rhythm, which may lead to sudden cardiac death. In someexamples, an IMD may deliver therapy to the patient to terminate orprevent a predicted tachyarrhythmia.

In one example, a device includes one or more electrodes, sensingcircuitry configured to sense a cardiac signal via the one or moreelectrodes, and processing circuitry configured to: determine an R-waveof the cardiac signal; determine a previous RR interval of the cardiacsignal based on the determined R-wave; determine a current RR intervalof the cardiac signal based on the determined R-wave; determine a searchwindow based on one or more of the current RR interval or the previousRR interval; determine a T-wave of the cardiac signal in the searchwindow; and determine a QT interval based on the determined T-wave andthe determined R-wave.

In another example, a method includes sensing a cardiac signal,determining an R-wave of the cardiac signal, determining a previous RRinterval of the cardiac signal based on the determined R-wave,determining a current RR interval of the cardiac signal based on thedetermined R-wave, determining a search window based on one or more ofthe current RR interval or the previous RR interval, determining aT-wave of the cardiac signal in the search window; and determining a QTinterval based on the determined T-wave and the determined R-wave.

In another example, a non-transitory, computer-readable storage mediumstoring a set of instructions that, when executed, cause a system todetermine an R-wave of the cardiac signal, determine a previous RRinterval of the cardiac signal based on the determined R-wave, determinea current RR interval of the cardiac signal based on the determinedR-wave, determine a search window based on one or more of the current RRinterval or the previous RR interval, determine a T-wave of the cardiacsignal in the search window, and determine a QT interval based on thedetermined T-wave and the determined R-wave.

The summary is intended to provide an overview of the subject matterdescribed in this disclosure. It is not intended to provide an exclusiveor exhaustive explanation of the systems, device, and methods describedin detail within the accompanying drawings and description below.Further details of one or more examples of this disclosure are set forthin the accompanying drawings and in the description below. Otherfeatures, objects, and advantages will be apparent from the descriptionand drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates the environment of an example medical system inconjunction with a patient.

FIG. 2 is a functional block diagram illustrating an exampleconfiguration of the insertable cardiac monitor (ICM) of the medicalsystem of FIG. 1.

FIG. 3 is a conceptual side-view diagram illustrating an exampleconfiguration of the ICM of FIGS. 1 and 2.

FIG. 4 is a functional block diagram illustrating an exampleconfiguration of the external device of FIG. 1.

FIGS. 5A and 5B are conceptual diagrams illustrating example primary andsecondary sensing channels for an R-wave and a T-wave detector accordingto the techniques of this disclosure.

FIG. 6 is a conceptual diagram illustrating an example snippet of an EGMsignal and a corresponding rectified waveform.

FIG. 7 is a conceptual diagram illustrating an example snippet of an EGMsignal depicting the different parameters that may be computed by the QTdetection algorithm according to the techniques of this disclosure.

FIG. 8 is a conceptual diagram illustrating an example snippet of an EGMsignal depicting the computation of the window_start and window_endparameters according to the techniques of this disclosure.

FIG. 9 is a conceptual diagram illustrating an example snippet of andEGM signal depicting the computation of window_start and window_endparameters in the case where the difference between the current andprevious RR interval is greater than 500 milliseconds (ms) according tothe techniques of this disclosure.

FIG. 10 is a conceptual diagram illustrating an example MATLAB GUI toaid in the manual annotation of a data set.

FIG. 11 is a conceptual diagram illustrating examples of manualannotations.

FIG. 12 is a conceptual diagram illustrating a histogram of thedifference between the QT interval based on the manual annotations andthe QT interval detected results based on the techniques of thisdisclosure for every beat in a development data set.

FIG. 13 is a conceptual diagram illustrating a histogram of thedifference between the QT corrected (QTc) interval based on the manualannotations and the QTc interval detected results based on thetechniques of this disclosure for every beat in the development dataset.

FIG. 14 is a conceptual diagram illustrating a histogram of the mean ofthe difference between the QTc interval based on the manual annotationsand the QTc interval detected results based on the techniques of thisdisclosure for 46 unique devices in the development data set.

FIGS. 15A-D are conceptual diagrams illustrating example EGM strips fromdevices for which a mean of (QTc interval(true)−QTc interval(algorithmdetected)) was greater than 25 ms depicting both manual annotation andthe detections according to the techniques of this disclosure.

FIG. 16 is a conceptual diagram illustrating example of EGM strips fromthe development data set having T-waves with different morphologies andorientations at different RR intervals depicting both manual annotationand the detections according to the techniques of this disclosure.

FIG. 17A-B is a conceptual diagram depicting examples of EGM strips fromthe development data set depicting beat to beat changes in QTc intervaldetected by both manual annotation as well as detection according to thetechniques of this disclosure.

FIGS. 18A-B are flow diagrams illustrating example techniques of thisdisclosure.

FIG. 19 is a flow diagram illustrating an example technique of thisdisclosure.

FIG. 20 is a flow diagram illustrating an example technique of thisdisclosure.

DETAILED DESCRIPTION

This disclosure describes techniques for identifying one or moreparameters of a cardiac signal, such as QT intervals. The parameters maybe used to, for example, detect or predict arrhythmias, to evaluateother conditions of the patient such as change in electrolytes, changein diabetic status, fluid overload or dehydration, or to configureand/or evaluate therapies, such as pharmacological therapies.

A T-wave represents ventricular repolarization. Repolarization of theventricles begins at the epicardial surface of the ventricles andprogresses inwardly through the ventricular walls to the endocardialsurface. The T-wave occurs during the last part of the ventricularsystole. The onset of the T-wave is the first or abrupt or gradualdeviation from the S-T segment. The point where the T-wave returns tothe baseline marks the end of the T-wave.

The QT interval on an electrocardiogram (ECG) is measured from thebeginning of the QRS complex to the end of the T-wave. The QT intervalrepresents the time it takes for the ventricles of the heart todepolarize and repolarize, or to contract and relax. This electricalactivity of the heart is mediated through channels, complex molecularstructures within the myocardial cell membrane that regulate the flow ofions in and out of cardiac cells. See, e.g., Viskin S., Long QTSyndromes and Torsade de Pointes, Lancet, Vol. 62(13), pp. 1625-1633,1999 (hereinafter Viskin). The rapid inflow of positively charged ions(sodium and calcium) results in normal myocardial depolarization. Whenthis inflow is exceeded by outflow of potassium ions, myocardialrepolarization occurs. Malfunction of ion channels leads to anintracellular excess of positively charged ions by way of an inadequateoutflow of potassium ions or excess inflow of sodium ions. Thisintracellular excess of positively charged ions extends ventricularrepolarization and results in QT interval prolongation. See, e.g.,Al-Khatib S M, et al., What Clinicians Should Know About the QTInterval, JAMA, Vol. 289(16), pp. 2120-2127, 2003 (hereinafterAl-Khatib).

An abnormally long or abnormally short QT interval is associated with anincreased risk of developing abnormal heart rhythms and sudden cardiacdeath. Abnormalities in the QT interval can be caused by geneticconditions such as Long QT syndrome, by certain medications such assotalol or pitolisant, by disturbances in the concentrations of certainsalts within the blood such as hypokalemia and hypomagnesemia, or byhormonal imbalances such as hypothyroidism, or changes in blood glucose.The normal QT interval varies depending on age and gender, and isusually in the order of about 0.36 to 0.44 seconds. Anything greaterthan or equal to 0.50 seconds may be considered dangerous for any age orgender. See, e.g., Cox, Natalie K. The QT Interval: How Long is tooLong?, Nursing Made Incredibly Easy, Vol. 9(2), pp. 17-21, 2011. The QTinterval of an ECG has gained clinical importance, primarily becauseprolongation of this interval can predispose a person to a potentiallyfatal ventricular arrhythmia known as torsades de pointes which can leadto sudden cardiac death as discussed in Viskin.

In a clinical setting, it is now widely recognized that a typicalmeasurement of the QT interval is subject to substantial variability,which can cloud interpretation. This variability in QT intervalmeasurement results from biological factors, such as diurnal effects,differences in autonomic tone, electrolytes, and drugs; technicalfactors, including the environment, the processing of the recording, andthe acquisition of the ECG recording; and intra-observer andinter-observer variability, resulting from variations in T-wavemorphology, noisy baseline, and the presence of U-waves. Interobservervariability also results from the lack of agreement among experts aboutstandardizing approaches to measure the QT interval. See e.g.,Al-Khatib; Morganroth J, et al., Variability of the QT Measurement inHealthy Men, with Implications for Selection of an Abnormal QT Value toPredict Drug Toxicity and Proarrhythmia, American Journal of Cardiology,Vol. 67(8), pp. 774-776, 1991; and Molnar J, et al., Diurnal Pattern ofQTc Interval: How Long is Prolonged? Possible Relation to CircadianTriggers of Cardiovascular Events, Journal of the American College ofCardiology, Vol. 27(1), pp. 76-83, 1996.

The QT interval of an ECG has gained clinical importance, primarilybecause prolongation of this interval can predispose a person to apotentially fatal ventricular arrhythmia known as torsades de pointeswhich could in turn lead to sudden cardiac death. Multiple factors havebeen implicated in causing QT prolongation and torsades de pointes.Among these, an important risk factor for long QT syndrome is the use ofQT prolonging drugs. A QT interval greater than 500 ms has been shown tocorrelate with higher risk of torsades de pointes as discussed inAl-Khatib.

Morganroth J, et al., Evaluation and Management of Cardiac Safety Usingthe Electrocardiogram in Oncology Clinical Trials: Focus on CardiacRepolarization (QTc Interval), Clinical Pharmacology and Therapeutics,Vol 87(2), pp. 166-74, 2010, recommends that a 10- to 20 ms QT corrected(QTc) change be considered clinically relevant and that patients in thisrange, especially those with QT-related risk factors, be safeguardedwith careful ECG assessment during treatment. Based on adequaterisk-benefit evaluation, the authors suggest that higher tolerancelimits for QTc prolonging effects may be acceptable for oncologicaldrugs as they meet patients' particular medical needs.

Chouchoulis k, et al., Impact of QT Interval Prolongation FollowingAntiarrhythmic Drug Therapy on Left Ventricular Function, FutureCardiology, Vol 13(1), 2016, assessed whether antiarrhythmicdrug-induced QT interval prolongation affects left ventricular function.The study population included 54 patients with symptomatic recent onsetatrial fibrillation spontaneously cardioverted to sinus rhythm.Significant QT maximum (QTmax) and QTc interval prolongation was noticedfollowing drug ingestion including Sotalol (from 424±40 ms to 460±57 msand from 446±35 ms to 474±48 ms, respectively, both p<0.01) andAmiodarone (from 437±41 ms to 504±39 ms and from 469±35 ms to 527±50 ms,respectively, both p<0.01). Thus, significant prolongation of QTc wasnoticed in this study following antiarrhythmic drugs such as Sotalol andAmiodarone.

Several studies also show a correlation between QT changes and diabetes.Compared to the general population, type 1 diabetes may increase therisk of mortality, due largely to an increased risk of cardiovasculardisease. Almost half of patients with type 1 diabetes have a prolongedQTc interval (>440 ms). Diabetes with a prolonged QTc interval wasassociated with a 29% mortality over 10 years in comparison to 19% witha normal QTc interval according to Rossing P, et al., Prolonged QTcInterval Predicts Mortality in Patients with Type 1 Diabetes Mellitus,Diabetic Medicine, Vol 18 (3), pp. 199-205, 2001.

One study stated that QTc dispersion is an important predictor ofcardiac mortality. In a Rotterdam Study (de Bruyne M C, et al., QTcDispersion Predicts Cardiac Mortality in the Elderly: the RotterdamStudy, Circulation, Vol 97(5), pp. 467-472, 1998), persons in thehighest tertile (>60 ms) relative to the lowest tertile (<39 ms) of QTcdispersion had a 2-fold increased risk of cardiac death. The RotterdamStudy also showed that QTc dispersion is larger in diabetic than innondiabetic persons according to Marfella, et al., QTc Dispersion,Hyperglycemia, and Hyperinsulinemia, Circulation, Vol 100(25), 1999(hereinafter Marfella).

Marfella evaluated the effect of acute hyperglycemia on QTc duration andQTc dispersion in 27 normal subjects. During glucose clampadministration to the subjects, plasma glucose stabilized at 15 mmol/L,and plasma insulin showed a biphasic pattern of response, with an earlyrise at 10 minutes (327±89 pmol/L) followed by a gradual and sustainedincrease (456±120 pmol/L). QTc increased from 413±26 to 442±29 ms(P<0.05) at the end of the clamp administration, and QTc dispersionincreased from 32±9 to 55±12 ms (P<0.01). This showed that acutehyperglycemia in normal subjects produces significant increases of QTcand QTc dispersion.

Lee S P, et al., Influence of Autonomic Neuropathy on QTc IntervalLengthening During Hypoglycemia in Type 1 Diabetes, Diabetes, Vol.53(6), pp. 1535-42, 2004 discussed a study with 28 adults with type 1diabetes and 8 nondiabetic control subjects. QTc was then measuredduring controlled hypoglycemia (2.5 mmol/l) using a hyperinsulinemicclamp. Mean (+/−SE) QTc lengthened from 377+/−9 ms (baseline) to amaximum during hypoglycemia of 439 +/−13 ms in diabetic participants(BRS+ subjects) and from 378 +/−5 to 439 +/−10 ms in control subjects.This study showed that hypoglycemia produces electrocardiographic QTclengthening, a predictor of arrhythmia risk and sudden death.

Thus, continuous monitoring of QT intervals may allow identification oflong QT intervals, which may indicate a need for medical intervention.In some examples, continuous monitoring of QT intervals may be performedusing an insertable cardiac monitor (ICM). This disclosure describes anexample algorithm which may monitor the QT interval with an ICM, such asa LINQ™ ICM by Medtronic plc, of Dublin, Ireland.

A variety of types of medical devices sense cardiac EGMs. Some medicaldevices that sense cardiac EGMs are non-invasive, e.g., using aplurality of electrodes placed in contact with external portions of thepatient, such as at various locations on the skin of the patient. Theelectrodes used to monitor the cardiac EGM in these non-invasiveprocesses may be attached to the patient using an adhesive, strap, belt,or vest, as examples, and electrically coupled to a monitoring device,such as an electrocardiograph, Holter monitor, or other electronicdevice. The electrodes are configured to sense electrical signalsassociated with the electrical activity of the heart or other cardiactissue of the patient, and to provide these sensed electrical signals tothe electronic device for further processing and/or display of theelectrical signals. The non-invasive devices and methods may be utilizedon a temporary basis, for example to monitor a patient during a clinicalvisit, such as during a doctor's appointment, or for example for apredetermined period of time, for example for one day (twenty-fourhours), or for a period of several days.

External devices that may be used to non-invasively sense and monitorcardiac EGMs include wearable devices with electrodes configured tocontact the skin of the patient, such as patches, watches, or necklaces.One example of a wearable physiological monitor configured to sense acardiac EGM is the SEEQ™ Mobile Cardiac Telemetry System, available fromMedtronic plc, of Dublin, Ireland. Such external devices may facilitaterelatively longer-term monitoring of patients during normal dailyactivities, and may periodically transmit collected data to a networkservice, such as the Medtronic Carelink™ Network.

Some implantable medical devices (IMDs) also sense and monitor cardiacEGMs. The electrodes used by IMDs to sense cardiac EGMs are typicallyintegrated with a housing of the IMD and/or coupled to the IMD via oneor more elongated leads. Example IMDs that monitor cardiac EGMs includepacemakers and implantable cardioverter-defibrillators, which may becoupled to intravascular or extravascular leads, as well as pacemakerswith housings configured for implantation within the heart, which may beleadless. An example of pacemaker configured for intracardiacimplantation is the Micra™ Transcatheter Pacing System, available fromMedtronic plc. Some IMDs that do not provide therapy, e.g., implantablepatient monitors, sense cardiac EGMs. One example of such an IMD is theLINQ™ ICM, which may be inserted subcutaneously. Such IMDs mayfacilitate relatively longer-term monitoring of patients during normaldaily activities, and may periodically transmit collected data to anetwork service, such as the Medtronic Carelink™ Network.

While this disclosure discusses techniques for measuring QT intervalswith an example ICM, any medical device configured to sense a cardiacEGM via implanted or external electrodes, including the examplesidentified herein, may implement the techniques of this disclosure formeasuring QT intervals. The techniques include evaluation of the cardiacEGM using criteria configured to provide a desired sensitivity andspecificity of QT interval detection despite noise and depolarizationmorphology variations due to varying electrode positions. The techniquesof this disclosure for identifying QT intervals may facilitatedeterminations of cardiac wellness, and risk of sudden cardiac death,and may lead to clinical interventions to suppress the risk of suddencardiac death.

FIG. 1 illustrates the environment of an example medical system 2 inconjunction with a patient 4, in accordance with one or more techniquesof this disclosure. The example techniques may be used with an ICM 10,which may be in wireless communication with at least one of externaldevice 12 and other devices not pictured in FIG. 1. In some examples,ICM 10 is implanted outside of a thoracic cavity of patient 4 (e.g.,subcutaneously in the pectoral location illustrated in FIG. 1). ICM 10may be positioned near the sternum near or just below the level of theheart of patient 4, e.g., at least partially within the cardiacsilhouette. ICM 10 includes a plurality of electrodes (not shown in FIG.1), and is configured to sense a cardiac EGM via the plurality ofelectrodes. In some examples, ICM 10 takes the form of the LINQ™ ICM, oranother ICM similar to, e.g., a version or modification of, the LINQ™ICM.

External device 12 may be a computing device with a display viewable bythe user and an interface for providing input to external device 12(i.e., a user input mechanism). In some examples, external device 12 maybe a notebook computer, tablet computer, workstation, one or moreservers, cellular phone, personal digital assistant, or anothercomputing device that may run an application that enables the computingdevice to interact with ICM 10. External device 12 is configured tocommunicate with ICM 10 and, optionally, another computing device (notillustrated in FIG. 1), via wireless communication. External device 12,for example, may communicate via near-field communication technologies(e.g., inductive coupling, NFC or other communication technologiesoperable at ranges less than 10-20 cm) and far-field communicationtechnologies (e.g., RF telemetry according to the 802.11 or Bluetooth®specification sets, or other communication technologies operable atranges greater than near-field communication technologies).

External device 12 may be used to configure operational parameters forICM 10. External device 12 may be used to retrieve data from ICM 10,such as QT intervals. The retrieved data may include values ofphysiological parameters measured by ICM 10, indications of episodes ofarrhythmia or other maladies detected by ICM 10, and physiologicalsignals recorded by ICM 10. For example, external device 12 may retrieveinformation related to detection of QT intervals by ICM 10, such as amean, median, minimum, maximum, range or mode of QT intervals over atime period. The time period may be predetermined, for example, hourly,daily or weekly, or may be otherwise based on the timing of the lastretrieval of information by external device 12, or may be determined bya user of external device 12, such as by entering a command on externaldevice 12 requesting the information from ICM 10. External device 12 mayalso retrieve cardiac electrogram (EGM) segments recorded by ICM 10,e.g., due to ICM 10 determining that an episode of arrhythmia or anothermalady occurred during the segment, or in response to a request torecord the segment from patient 4 or another user.

Processing circuitry of medical system 2, e.g., of ICM 10, externaldevice 12, and/or of one or more other computing devices, may beconfigured to perform the example techniques of this disclosure fordetermining QT intervals. In some examples, the processing circuitry ofmedical system 2 may analyze a cardiac EGM sensed by ICM 10 to determineQT intervals in the cardiac EGM. Although described in the context ofexamples in which ICM 10 that senses the cardiac EGM comprises aninsertable cardiac monitor, example systems including one or moreimplantable or external devices of any type configured to sense acardiac EGM may be configured to implement the techniques of thisdisclosure.

FIG. 2 is a functional block diagram illustrating an exampleconfiguration of ICM 10 of FIG. 1 in accordance with one or moretechniques described herein. In the illustrated example, ICM 10 includeselectrodes 16A and 16B (collectively “electrodes 16”), antenna 26,processing circuitry 50, sensing circuitry 52, communication circuitry54, storage device 56, switching circuitry 58, and sensors 62. Althoughthe illustrated example includes two electrodes 16, IMDs including orcoupled to more than two electrodes 16 may implement the techniques ofthis disclosure in some examples.

Processing circuitry 50 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 50 may includeany one or more of a microprocessor, a controller, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or equivalent discrete or analoglogic circuitry. In some examples, processing circuitry 50 may includemultiple components, such as any combination of one or moremicroprocessors, one or more controllers, one or more DSPs, one or moreASICs, or one or more FPGAs, as well as other discrete or integratedlogic circuitry. The functions attributed to processing circuitry 50herein may be embodied as software, firmware, hardware or anycombination thereof.

Sensing circuitry 52 may be selectively coupled to electrodes 16 viaswitching circuitry 58, e.g., to select the electrodes 16 and polarity,referred to as the sensing vector, used to sense a cardiac EGM, ascontrolled by processing circuitry 50 Sensing circuitry 52 may sensesignals from electrodes 16, e.g., to produce a cardiac EGM, in order tofacilitate monitoring the electrical activity of the heart. Sensingcircuitry 52 also may monitor signals from sensors 62, which may includeone or more accelerometers, pressure sensors, and/or optical sensors, asexamples. In some examples, sensing circuitry 52 may include one or morefilters and amplifiers for filtering and amplifying signals receivedfrom electrodes 16 and/or sensors 62.

Sensing circuitry 52 and/or processing circuitry 50 may be configured todetect R-waves and T-waves. Sensing circuitry 52 may include one or morerectifiers, filters, amplifiers, comparators, and/or analog-to-digitalconverters, in some examples. In some examples, sensing circuitry 52 mayoutput an indication to processing circuitry 50 in response to sensingan R-wave or a T-wave. In some examples, processing circuitry 50 maydetermine an R-wave or a T-wave in an indication from sensing circuitry52. Processing circuitry 50 may use the indications of detected R-wavesand T-waves for determining QT intervals or corrected QT intervals(QTc).

Sensing circuitry 52 may also provide one or more digitized cardiac EGMsignals to processing circuitry 50 for analysis, e.g., for use incardiac rhythm discrimination, and/or for analysis to determine QTintervals or QTc intervals according to the techniques of thisdisclosure. In some examples, processing circuitry 50 may store thedigitized cardiac EGM in storage device 56. Processing circuitry 50 ofICM 10, and/or processing circuitry of another device that retrievesdata from ICM 10, may analyze the cardiac EGM to determine QT intervalsor QTc intervals according to the techniques of this disclosure.

Communication circuitry 54 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as external device 12, another networked computing device,or another IMD or sensor. Under the control of processing circuitry 50,communication circuitry 54 may receive downlink telemetry from, as wellas send uplink telemetry to external device 12 or another device withthe aid of an internal or external antenna, e.g., antenna 26. Inaddition, processing circuitry 50 may communicate with a networkedcomputing device via an external device (e.g., external device 12) and acomputer network, such as the Medtronic plc CareLink® Network. Antenna26 and communication circuitry 54 may be configured to transmit and/orreceive signals via inductive coupling, electromagnetic coupling, NearField Communication (NFC), Radio Frequency (RF) communication,Bluetooth®, WiFi, or other proprietary or non-proprietary wirelesscommunication schemes.

In some examples, storage device 56 includes computer-readableinstructions that, when executed by processing circuitry 50, cause ICM10 and processing circuitry 50 to perform various functions attributedto ICM 10 and processing circuitry 50 herein. Storage device 56 mayinclude any volatile, non-volatile, magnetic, optical, or electricalmedia, such as a random access memory (RAM), read-only memory (ROM),non-volatile RAM (NVRAM), electrically-erasable programmable ROM(EEPROM), flash memory, or any other digital media. Storage device 56may store, as examples, programmed values for one or more operationalparameters of ICM 10 and/or data collected by ICM 10 for transmission toanother device using communication circuitry 54. Data stored by storagedevice 56 and transmitted by communication circuitry 54 to one or moreother devices may include premature ventricular contraction (PVC)detection quantifications and/or digitized cardiac EGMs, as examples.

FIG. 3 is a conceptual side-view diagram illustrating an exampleconfiguration of ICM 10 of FIGS. 1 and 2. In the example shown in FIG.3, ICM 10 may include a leadless, subcutaneously-implantable monitoringdevice having a housing 15 and an insulative cover 76. Electrode 16A andelectrode 16B may be formed or placed on an outer surface of cover 76.Circuitries 50-62, described above with respect to FIG. 2, may be formedor placed on an inner surface of cover 76, or within housing 15. In theillustrated example, antenna 26 is formed or placed on the inner surfaceof cover 76, but may be formed or placed on the outer surface in someexamples. In some examples, one or more of sensors 62 may be formed orplaced on the outer surface of cover 76. In some examples, insulativecover 76 may be positioned over an open housing 15 such that housing 15and cover 76 enclose antenna 26 and circuitries 50-62, and protect theantenna and circuitries from fluids such as body fluids.

One or more of antenna 26 or circuitries 50-62 may be formed on theinner side of insulative cover 76, such as by using flip-chiptechnology. Insulative cover 76 may be flipped onto a housing 15. Whenflipped and placed onto housing 15, the components of ICM 10 formed onthe inner side of insulative cover 76 may be positioned in a gap 78defined by housing 15. Electrodes 16 may be electrically connected toswitching circuitry 58 through one or more vias (not shown) formedthrough insulative cover 76. Insulative cover 76 may be formed ofsapphire (i.e., corundum), glass, parylene, and/or any other suitableinsulating material. Housing 15 may be formed from titanium or any othersuitable material (e.g., a biocompatible material). Electrodes 16 may beformed from any of stainless steel, titanium, platinum, iridium, oralloys thereof. In addition, electrodes 16 may be coated with a materialsuch as titanium nitride or fractal titanium nitride, although othersuitable materials and coatings for such electrodes may be used.

FIG. 4 is a block diagram illustrating an example configuration ofcomponents of external device 12. In the example of FIG. 4, externaldevice 12 includes processing circuitry 80, communication circuitry 82,storage device 84, and user interface 86.

Processing circuitry 80 may include one or more processors that areconfigured to implement functionality and/or process instructions forexecution within external device 12. For example, processing circuitry80 may be capable of processing instructions stored in storage device84. Processing circuitry 80 may include, for example, microprocessors,DSPs, ASICs, FPGAs, or equivalent discrete or integrated logiccircuitry, or a combination of any of the foregoing devices orcircuitry. Accordingly, processing circuitry 80 may include any suitablestructure, whether in hardware, software, firmware, or any combinationthereof, to perform the functions ascribed herein to processingcircuitry 80.

Communication circuitry 82 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as ICM 10. Under the control of processing circuitry 80,communication circuitry 82 may receive downlink telemetry from, as wellas send uplink telemetry to, ICM 10, or another device. Communicationcircuitry 82 may be configured to transmit or receive signals viainductive coupling, electromagnetic coupling, Near Field Communication(NFC), Radio Frequency (RF) communication, Bluetooth®, WiFi, or otherproprietary or non-proprietary wireless communication schemes.Communication circuitry 82 may also be configured to communicate withdevices other than ICM 10 via any of a variety of forms of wired and/orwireless communication and/or network protocols.

Storage device 84 may be configured to store information within externaldevice 12 during operation. Storage device 84 may include acomputer-readable storage medium or computer-readable storage device. Insome examples, storage device 84 includes one or more of a short-termmemory or a long-term memory. Storage device 84 may include, forexample, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories,or forms of EPROM or EEPROM. In some examples, storage device 84 is usedto store data indicative of instructions for execution by processingcircuitry 80. Storage device 84 may be used by software or applicationsrunning on external device 12 to temporarily store information duringprogram execution.

Data exchanged between external device 12 and ICM 10 may includeoperational parameters. External device 12 may transmit data includingcomputer readable instructions which, when implemented by ICM 10, maycontrol ICM 10 to change one or more operational parameters and/orexport collected data, such as QT intervals or QTc intervals. Forexample, processing circuitry 80 may transmit an instruction to ICM 10which requests ICM 10 to export collected data (e.g., QT interval data,QTc interval data and/or digitized cardiac EGMs) to external device 12.In turn, external device 12 may receive the collected data from ICM 10and store the collected data in storage device 84. Processing circuitry80 may implement any of the techniques described herein to analyzecardiac EGMs received from ICM 10, e.g., to determine QT intervals orQTc intervals.

A user, such as a clinician or patient 4, may interact with externaldevice 12 through user interface 86. User interface 86 includes adisplay (not shown), such as a liquid crystal display (LCD) or a lightemitting diode (LED) display or other type of screen, with whichprocessing circuitry 80 may present information related to ICM 10, e.g.,cardiac EGMs, indications of QT intervals or QTc intervals. In addition,user interface 86 may include an input mechanism to receive input fromthe user. The input mechanisms may include, for example, any one or moreof buttons, a keypad (e.g., an alphanumeric keypad), a peripheralpointing device, a touch screen, or another input mechanism that allowsthe user to navigate through user interfaces presented by processingcircuitry 80 of external device 12 and provide input. In other examples,user interface 86 also includes audio circuitry for providing audiblenotifications, instructions or other sounds to the user, receiving voicecommands from the user, or both.

FIGS. 5A and 5B are conceptual diagrams illustrating example primary andsecondary sensing channels for R-wave detection and a T-wave detector.In FIG. 5A, sensing circuitry 52 of ICM 10 may sense R-waves by usingdual channel sensing techniques of FIGS. 5A and 5B. Cardiac signals(e.g., signals from electrode 16A and electrode 16B) may be filtered byband-pass filter 100. In some examples, band-pass filter 100 may have apassband in the range of about 10 Hz to 32 Hz. In some examples,band-pass filter 100 may have a non-linear response as shown. In otherexamples, band-pass filter 100 may have a generally linear response. Theband-passed signal may then be rectified by rectifier 102.

The rectified signal may then be input to an auto adjusting thresholdprocess 104. For example, auto adjusting threshold process may sense anevent has occurred in the cardiac signal when the amplitude of rectifiedsignal from rectifier 102 exceeds the auto adjusted threshold. Autoadjusting threshold process 104 may use an auto adjusting sensitivitywith a short blanking period (e.g., in the order of 150 ms). During ablanking period, a sensing process, such as auto adjusting thresholdprocess 104 or fixed threshold process 106, may not sense an event inthe cardiac signal so as to avoid a single depolarization from resultingin multiple sensed events. Auto adjusting threshold process 104 may formthe primary sensing channel 108, which may be the main R-wave sensingmechanism in ICM 10, and may be configured to accommodate the detectionof both tachyarrhythmia and bradyarrhythmia.

Once primary sensing channel 108 detects an R wave, the threshold ofauto adjusting threshold process 104 is set at 65% of the amplitude ofthe detected R wave (which may be a relatively high threshold so that Rwaves are not detected immediately). Then the threshold decays from the65% value to 35 microvolt so that the next R wave may be detected. Insome examples, there may be points where the threshold drops sharplysuch as after anticipated T-waves and P-waves to avoid oversensing ofT-waves and/or P-waves.

In some examples, the rectified signal may be input into a fixedthreshold process 106. Fixed threshold process 106 may have a fixedthreshold and a relatively longer blanking period (e.g., in the order of520 ms) than auto adjusting threshold process 104 to reduceunder-sensing. Similar to auto adjusting threshold process 104, fixedthreshold process 106 may sense an event in the cardiac signal when theamplitude of the rectified signal exceeds the fixed threshold. Theoutput of fixed threshold process 106 may form a secondary sensingchannel 110. In other examples (not shown), secondary sensing channel110 may use different filtering and/or different rectification thanprimary sensing channel 108.

The example dual channel sensing scheme of FIGS. 5A and 5B may beemployed to avoid under sensing some R-waves, such as those in PVCbeats. To capture these beats, a secondary channel, such as secondarysensing channel 110 used with a lower threshold may be used.

For example, when primary sensing channel 108 senses a R-wave, primarysensing channel 108 may blank auto adjusting threshold process 104, aswell as the fixed threshold process 106, for a time period, such as 150ms, to avoid secondary sensing channel 110 from sensing the same beat.If secondary sensing channel 110 senses a R-wave which was not sensed byprimary sensing channel 108, secondary sensing channel 110 may blankfixed threshold process 106 for 520 ms after the R-wave sense. In thisexample, secondary sensing channel 106 may not blank the primary channelfrom sensing.

To determine the T-wave location, ICM 10 may band-pass the EGM signal,from electrode 16A and electrode 16B, e.g., using band-pass filter 90.In some examples, the band-pass filter may be a 6-20 Hz band-passfilter. The band-passed signal may be rectified by rectifier 92. In FIG.5B, primary sensing channel 108 and secondary sensing channel 110determine R-wave senses 95. R-wave senses 95 may be utilized by T-wavesensor 94 to determine a search window for a T-wave.

FIG. 6 is a conceptual diagram illustrating an example snippet of an EGMsignal 105 and a corresponding rectified waveform 107.

According to the techniques of this disclosure, ICM 10 may determine thewindow after the QRS complex to search for the T-wave. ICM 10 maydetermine the window based on one or more of the current RR interval(the time between two successive R-waves) and the RR interval of theprevious beat (the previous RR interval). To accurately determine thestarting and ending sample of the search window, ICM 10 may determinethe R-wave peak sample in the rectified signal (e.g., in primary sensingchannel 108 and/or secondary sensing channel 110). To determine theR-wave peak in the rectified signal, processing circuitry 50 of ICM 10may take a first predetermined number of samples, such as 14 samples,before the sensed R-wave and a second predetermined number of samples,such as 25 samples, after the sensed R-wave at a predeterminedfrequency, such as 256 Hz, and determine that the sample with themaximum amplitude is the R-wave peak sample in the rectified signal. Insome examples, the first predetermined number of samples and the secondpredetermined number of samples may be the same. In other examples, thefirst predetermined number of samples and the second predeterminednumber of samples may be different. ICM 10 may use this technique todetermine the R-wave peak samples of the current, previous and the nextbeat as parameters peak_current_sample, peak_previous_sample andpeak_next_sample, respectively, in order to obtain the current and theprevious RR intervals. ICM may determine the R-wave peak samples of thecurrent, previous and next beat to improve the accuracy of the windowdetermination process in which the T-wave location is searched.

After determining the R-wave peak samples, ICM 10 may determine fourparameters used for determining the T-wave search window. ICM 10 maydetermine the parameters of window_start_current and window_end_currentbased on the current RR interval between the current and the next beat.Similarly, ICM 10 may determine the parameters window_start_previous andwindow_end_previous based on the previous RR interval between thecurrent beat and previous beat. Table 1 and Table 2 below show examplesof the determination of these four parameters based on the current andprevious RR interval.

TABLE 1 Determination of window_start_current and window_end_currentparameters based on the current RR interval Current RR intervalWindow_start_current Window_end_current (samples at 256 Hz) parameterparameter  <=75 27 20  >=75 and <=105 29 25   >105 and <=125 40 40  >125and <150 45 55 >=150 and <170 50 60 >=170 and <190 55 65 >=190 and <22055 75 >=220 and <240 60 80 >=240 and <260 60 85 >=260 and <300 7090 >=300 and <350 70 95 >=350 75 100

TABLE 2 Determination of window_start_previous and window_end_previousparameters based on the previous RR interval Current RR intervalWindow_start_current Window_end_current (samples at 256 Hz) parameterparameter  <=75 27 20  >=75 and <=105 29 25   >105 and <=125 40 40  >125and <150 45 55 >=150 and <170 50 60 >=170 and <190 55 65 >=190 and <22055 75 >=220 and <240 60 80 >=240 and <260 60 85 >=260 and <300 7090 >=300 and <350 70 95 >=350 75 100

After determining these parameters, the length of the window based onthe previous RR interval was determined by:

window_start1=peak_previous_sample+window_start_previous

window_end1=peak_current_sample−window_end_previous

previous_window_length=window_end1−window_start1

Similarly, the length of the window based on the current RR interval wasdetermined by:

window_start2=peak_current_sample+window_start_current

window_end2=peak_next_sample−window_end_current

current_window_length=window_end2−window_start2

FIG. 7 is a conceptual diagram illustrating an example snippet of an EGMsignal depicting the different parameters computed by the QT detectionalgorithm according to the techniques of this disclosure. In the exampleof FIG. 7, ICM 10 may determine the starting and ending sample of thesearch window for the T-wave for the current beat by:

window_start=peak_current_sample+window_start_previous

window_end=window_start+previous_window_length.

FIG. 8 is a conceptual diagram illustrating an example snippet of an EGMsignal depicting the determination of the window_start and window_endparameters. The starting and ending samples for the search window may beinitially determined based on the previous RR interval as shown in FIG.8. For example, ICM 10 may determine if the condition (window_end>peak_next_sample-a_predetermined_number_of_samples (e.g., 60)) issatisfied, and if the condition is satisfied, ICM may set the parameterwindow_end to:

window_end=window_end2.

ICM 10 may set the parameter window_end=window_end2 to ensure that theP-wave of the next beat is not incorrectly determined to be the T-wave.Thus, if the end of the window as determined using the previous RRinterval is close to the QRS complex of the next beat, then the end ofthe window may be set based on the current RR interval instead of theprevious RR interval.

If the difference between the previous and current RR intervals isgreater than a third predetermined number of samples, such as a numberof samples for example, 128 samples at 256 Hz (500 ms), then ICM 10 mayset the window_start and window_end parameters to:

window_start=window_start2

window_end=window_end2

The third predetermined number of samples may be any number of samples,e.g., 102 samples at 256 Hz (400 ms); 154 samples at 256 Hz (600 ms) or205 samples at 256 Hz (800). In some examples, the frequency may bedifferent than 256 Hz.

FIG. 9 is a conceptual diagram illustrating an example snippet of an EGMsignal depicting the computation of window_start and window_endparameters in the case where the difference between the current andprevious RR interval is greater than 500 ms (128 samples at 256 Hz). Ifthe difference between the previous and current RR intervals is greaterthan the third predetermined number of samples, such as 128 samples (500ms), then ICM 10 may determine the window start and end samples based onthe current RR interval instead of the previous RR interval for accuracyas shown in FIG. 9. Similarly, if the previous beat was determined to benoisy, then ICM 10 may determine the window start and end samples basedon the current RR interval instead of the previous RR interval.

ICM 10 may determine the search window for the T-wave to start from thewindow_start sample and to end at window_end sample. To determine thelocation of the T-wave, for each sample in the window, a fourthpredetermined number of samples, such as a 9-sample median, may be taken(e.g., 4 samples before and 4 samples after a given sample at 256 Hz).ICM 10 may determine the sample with the maximum median value is to bethe location of the T-wave. In some examples, ICM 10 may compute the QTinterval from the peak_current_sample to this maximum median sample. Forexample, processing circuitry 50 may determine the QT intervaldetermining a time or a number of samples between peak_current_sampleand the maximum median sample.

Normal cardiac repolarization adapts to heart rate. This phenomenonmeans that with increasing heart rate, the myocardium remains constantlyexcitable, e.g., completely repolarized, before the next depolarizationwave occurs. This prevents incomplete repolarization and the subsequentpossibility for re-entrant tachycardia. In Long QT Syndrome, cardiacadaptation to changes in heart rate is disrupted, which promotesarrhythmias. See Postema P G, et al. The Measurement of the QT Interval,Current Cardiology Reviews, Vol 10(3), pp. 287-294, 2014.

The QT interval is dependent on the RR interval and is longer when theheart rate is slower and shorter when the heart rate is faster. So ICM10 may calculate a corrected QT interval (QTc). Using QTc may improvethe detection of patients at increased risk of ventricular arrhythmia.Three methods—Bazett's formula, Fridericia's formula, and Framingham'sformula are commonly used to compute QTc intervals:

QTc=QT/√RR   Bazett formula:

QTc=QT/RR ^(1/3)   Fridericia formula:

QTc=QT+0.154 (1−RR)   Framingham formula:

Even though Bazett's formula (logarithmic corrections) is the mostcommonly used QT correction formula, this formula is not optimal outsideof the 60-100 heart rate range. This formula over-corrects at heartrates greater than 100 bpm and under-corrects at heart rates lesser than60 bpm. Both the Friderica and the Framingham formula perform better forheart rates outside of the 60-100 range. A study showed that theFridericia and the Framingham correction formulas showed better ratecorrection and significantly improved prediction of 30-day and 1-yearmortality when compared with the Bazett formula. See Vandenberk B, etal., Which QT Correction Formulae to Use for QT Monitoring?, Journal ofthe American Heart Association, Vol 5(6), 2016. For example, ICM 10 mayemploy the Framingham formula (a linear correction formula) forcomputing the QTc interval. In other examples, ICM 10 may employ theFriderica formula. In other examples, ICM 10 may employ the Bazettformula. In other examples, ICM 10 may employ some other formula ortechniques to determine the QTc interval. In some examples, ICM 10 mayemploy more than one of the Framingham formula, the Friderica formula,or the Bazett formula. In such examples, ICM 10 may determine a mean,median or mode QTc based on the formulas used.

A QT detection algorithm which may be implemented in ICM 10 has beendeveloped by using real world clinical data from the de-identifiedMedtronic plc CareLink™ data warehouse. The algorithm was developedusing 74 nightly transmission episodes (each 10 seconds long) frompatients with Diabetes and Long QT syndrome and 70 patient activatedepisodes (30 second EGM snippets) during a follow-up of 1 year frompatients implanted with an ICM, such as ICM 10, for the syncopeindication in March of 2014 (Reveal LINQ™). The development data set hadover 3,800 beats from over 45 patients for analysis. This data setprovided T-wave morphologies at different positions and orientationsfrom the ICM.

After extracting the EGM from patient activated episodes, the R-waveswere sensed by running an algorithm according to the techniques of thisdisclosure. The primary and secondary event markers were used tomanually annotate the location of T-waves to obtain the manual data. Thealgorithm results were compared with the manually annotated locations ofthe T-waves to evaluate the performance of the QT detection algorithm.

FIG. 10 is a conceptual diagram illustrating an example GUI to aid inthe manual annotation of a data set. The EGM file to be annotated andthe markers generated by the algorithm may be input to the GUI. The GUImay display the beats for which the location of the T-waves should bemarked by a user. For every R-wave marker 112 displayed on the GUI, theuser may select the location of the T-wave 114 by clicking on it. Inaddition, the user may also assign an annotation to each beat as: 1) anormal beat; 2) a noisy T-wave; or 3) a wrong R-wave marker.

FIG. 11 is a conceptual diagram illustrating examples of manualannotations. After annotating all the R-waves (e.g., adding R-wavemarkers 112) on the GUI window, the user may press the Enter button tocontinue to the next set of R-waves that need to be annotated. FIG. 11shows annotation 1 of a normal beat, annotation 2 of a noisy beat andannotation 3 with incorrect annotations of R-waves 116.

FIG. 12 is a conceptual diagram illustrating a histogram of thedifference between the QT interval based on the manual annotations andthe QT interval computed based on the detected techniques of thisdisclosure for every beat in the development data set according totechniques of this disclosure. To determine the performance of the QTinterval detection techniques of this disclosure, the difference betweenthe QT interval computed based on the manual annotation and the QTinterval computed based on the detection techniques of this disclosure(QT(true)−QT(detected)) was computed for every beat in the developmentdata set as shown in FIG. 12. The mean of the absolute value of thisparameter was 22.7 ms and the median value was 11.7 ms. The mode wasfound to be 4 ms. It should be noted that the resolution of QTcomputation was 4 ms because the EGM data is at 256 Hz.

Table 3 below shows the percentage of beats in the development datacorresponding to the absolute value of the parameter(QT(true)−QT(detected)) for every beat in the development data set bothin terms of samples and ms where the total number of beats was 3829.

TABLE 3 Percentage of beats in the development data set corresponding tothe value of the difference between QT interval based on manualannotation and the QT interval based on the algorithm detection both interms of ms and the number of samples at 256 Hz. Absolute of (QT(true) −QT(detected)) Samples at 256 Hz Number of beats % of beats <=2 177846.4% <=4 2603  68% <=6 2999 78.3% <=8 3186 83.2% <=10  3271 85.4% <=12 3320 86.7% Absolute of (QT(true) − QT(detected)) in ms Number of beats %of beats <5 1106  29% <10 1778 46.4% <15 2247 58.7% <20 2844 74.3% <252999 78.3% <30 3126 81.6% <35 3186 83.2% <40 3294  86%

FIG. 13 is a conceptual diagram illustrating a histogram of thedifference between the QTc interval based on the manual annotations andthe QTc interval calculated based on the detection techniques of thisdisclosure for every beat in the development data set. It can beobserved in FIG. 13 and table 4 below that the difference between the QTinterval based on manual annotation and the QT interval based on thedetection techniques of this disclosure was less than or equal to 2samples (7.8 ms) for 46.5% of beats in the development data set. Thisparameter was less than 25 ms for over 78% of the beats in thedevelopment data set.

TABLE 4 Percentage of beats in the development data set corresponding tothe value of difference between the QTc interval based on manualannotation and the QTc interval based on the algorithm detection in ms.Absolute of (QT(true) − QT(detected)) in ms Number of beats % of beats<5 1106  29% <10 1778 46.4% <15 2247 58.7% <20 2712 70.8% <25 2999 78.3%<30 3126 81.6% <35 3187 83.2% <45 3294  86%

Using the Framingham correction formula, QTc interval was computed forevery beat in the development data set. Similar to the example of FIG.12, the difference between the QTc interval computed based on the manualannotation and the QTc interval based on the detection techniques ofthis disclosure was computed for every beat in the development data set.The histogram for this parameter is shown in FIG. 13. The mean of theabsolute value of this parameter was 22.6 ms and the median value was 12ms. The mode was found to be 4 ms.

FIG. 14 is a conceptual diagram illustrating a histogram of the mean ofthe difference between the QTc interval based on the manual annotationsand the QTc interval based on the detection techniques of thisdisclosure for 46 unique devices in the development data set. Todetermine the performance of the techniques of this disclosure based onunique devices, the parameter of (QTc interval (true)−QTc interval(algorithm detected)) was computed for beats in every unique ICMseparately and the mean of this parameter was computed for every device.It can be observed in FIG. 14 that 37 out of the 46 devices had a meanof less than 25 ms.

FIGS. 15A-D are conceptual diagrams illustrating example devices forwhich mean of (QTc interval(true)−QTc interval(algorithm detected)) wasgreater than 25 ms. There were 9 devices which had a mean greater than25 ms. Some examples from these specific ICMs are shown in FIGS. 15A-D.In several of these ICMs, as shown in FIG. 15A and in some instances inFIG. 15B, the techniques of this disclosure detected T-waves in adifferent location when compared to the manual annotation, but theT-waves were consistently detected generally in the same location. Forexample, in FIGS. 15A and 15C, R-wave markers 112 were consistentlyfollowed by T-waves 118 determined by manual annotation, followed byT-waves determined according to the detection techniques of the presentdisclosure 120. This pattern continues throughout FIGS. 15A and 15C. So,changes in QT interval may still be measured in these cases even thoughthe mean of (QTc interval (true)−QTc interval (algorithm detected)) waslarge in these devices.

In certain ICMs, such as shown in FIG. 15D, there were some variationsin detection of T-waves from beat to beat which in some cases was causeddue to noise or RR interval variability from beat to beat.

FIG. 16 is a conceptual diagram illustrating example of EGM strips fromthe development data set having T-waves with different morphologies andorientations at different RR intervals depicting both manual annotationand detections according to the techniques of this disclosure. Theseexamples include both nightly transmissions as well as patient activatedepisodes from ICMs for which the QT interval was accurately detected bythe algorithm.

Another observation from the analysis of the development data set wasthat the ICMs were able to capture beat to beat changes in the QTinterval well as shown in FIGS. 17A-B. In FIG. 17A, the QTc changes by43 ms between beat 122 and beat 124 which was captured well by the ICM.Similarly, in FIG. 17B, the QTc interval changes by 54 ms between beat126 and beat 128 and changes by 15 ms between beat 128 and the next beatwhich was captured by ICM 10 as well.

In some examples, the amplitude of the samples in the search window maybe weighted by giving more weight to (weighting more heavily) theamplitude of samples located where the T-wave is most likely to bedetected based on the previous QT intervals or QTc intervals, such as 12QT intervals or QTc intervals, and giving less weight to the samples atthe end of the window. For example, processing circuitry 50 of ICM 10may take the last 12 QT intervals or QTc intervals and form a weightingwindow based on the location of the T-wave in the fastest QT interval orQTc interval and the location of the T-wave in the slowest QT intervalor QTc interval and apply a weight to the amplitude of samples inside ofthe window so that the amplitude of samples is greater than theamplitude otherwise would be. For example, processing circuitry 50 ofICM 10 may apply a weight to the amplitude of samples outside of thewindow so that the amplitude of samples is less than the amplitudeotherwise would be. In some examples, the weighting window may be equalto the location of the T-wave in the fastest QT interval or QTc intervaland the location of the T-wave in the slowest QT interval or QTcinterval. In other examples, the weighting window may be larger than orsmaller than the location of the T-wave in the fastest QT interval orQTc interval and the location of the T-wave in the slowest QT intervalor QTc interval. In some examples, noise detection may be incorporatedby ICM 10 to detect noisy beats by detecting noisy QRS complexes anddetermine if the search window for the T-wave after the QRS is noisy.

In some examples, ICM 10 may employ a threshold for the amplitude forT-wave detection based on a predetermined number of previous T-wavedetections, such as 12, to ensure that P-waves or noise is not beingdetected as a T-wave. For example, processing circuitry 50 of ICM 10 maydetermine whether an amplitude of a sample in the search window is lessthan a threshold and based on the amplitude of the sample being lessthan the threshold, determine that the sample is not the T-wave. In someexamples, processing circuitry 50 of ICM 10 may determine a confidencelevel of the detected T-waves. For example, processing circuitry 50 ofICM 10 may determine the confidence level based on one or more of apredetermined number of previous T-wave amplitudes, QT intervals or QTcintervals, such as 12. If the amplitude of the detected T-wave is toolow compared to previous T-waves or if the QT interval (or QTc interval)is very different from the previous 12 QT intervals (or QTc intervals),then ICM 10 may give the detected T-wave a low confidence level.

In some examples, processing circuitry 50 of ICM 10 may determine amean, median, mode, standard deviation or any other trend of determinedQT intervals or QTc intervals over time. ICM 10 may communicate themean, median, mode, standard deviation or any other trend of thedetermined QTc intervals to external device 12. In some examples, ICM 10may determine a time or count of which QT intervals or QTc intervals arelonger than a predetermined threshold. This predetermined threshold maybe in the order of 500 ms, for example, as QT intervals greater than 500ms may correlate to a higher risk of torsades de pointes. In someexamples, ICM 10 may determine a time or count of QT intervals or QTcintervals that change greater than a threshold. For example, ICM 10 maydetermine a time or count of QT interval or QTc intervals that changegreater than 30 ms or 40 ms or some other threshold (which may even bepatient specific) within a specific time period.

FIG. 18A is a flow diagram illustrating example techniques of thisdisclosure. Sensing circuitry 52 of ICM 10 may sense a cardiac signal(130). In some examples, sensing circuitry 52 may apply one or moreband-pass filters (e.g., band-pass filter 100) or rectifiers (e.g.,rectifier 102) to a cardiac signal. In some examples, sensing circuitry52 may use a primary sensing channel 108 and a secondary sensing channel110 to sense the cardiac signal.

Sensing circuitry 52 may determine an R-wave of the cardiac signal(132). For example, auto adjusting threshold process 104 and/or fixedthreshold process 106 may determine a rectified signal from rectifier102 has exceeded the auto adjusting threshold and/or the fixedthreshold. Processing circuitry 50 of ICM 10 may determine a previous RRinterval (134). For example, to accurately determine the previous RRinterval, processing circuitry 50 may determine a peak R value in a twoconsecutive sensed R-waves (e.g., peak_current_sample andpeak_previous_sample). Processing circuitry 50 may determine theprevious RR interval to be the time between the two consecutive peak Rvalues, peak_current_sample and peak_previous_sample. In some examples,processing circuitry 50 may take a first predetermined number of samplesbefore the sensed R-wave and a second predetermined number of samplesafter the sensed R-wave at a predetermined frequency and determine thesample with the maximum amplitude is the R-wave peak sample.

Processing circuitry 50 of ICM 10 may also determine a current RRinterval (136). For example, to accurately determine the current RRinterval, processing circuitry 50 may determine a peak R value in for anext sensed R-wave (e.g., peak_next_sample). Processing circuitry 50 maydetermine peak_next_sample in the same or similar manner to that used todetermine peak_current_sample and peak_previous_sample. Processingcircuitry 50 may determine the current RR interval to be the timebetween the two consecutive peak R values, peak_next_sample andpeak_current_sample.

Processing circuitry 50 may then determine a search window to search fora T-wave based on one or more of the current RR interval or the previousRR interval. Processing circuitry 50 may determine the search window tostart at a number of samples after an R-wave peak and end a differentnumber of samples after the R-wave peak. These numbers of samples may bebased on the length of the current RR interval or the previous RRinterval. In some examples, the numbers of samples may be stored instorage device 56, such as in a look-up table. In some examples, thenumbers of samples may be those set forth above in Tables 1 and 2.

In some examples, processing circuitry 50 may utilize a threshold toassist in determining a T-wave. In other examples, processing circuitrymay not utilize a threshold. For example, processing circuitry 50 maydetermine whether an amplitude of a sample is less than a threshold (orin some instances less than or equal to) (140). If the amplitude of asample is less than the threshold (or in some instances less than orequal to) (the “YES” path in FIG. 18A), processing circuitry 50 maydetermine the sample is not a T-wave (142). Processing circuitry 50 maythen examine the next sample. If the amplitude of a sample is equal toor greater than the threshold (or in some instances greater than),processing circuitry 50 may keep that sample as a candidate for asT-wave (the “NO” path of FIG. 18A).

Processing circuitry 50 may determine a T-wave of the cardiac signal inthe search window (144). For example, processing circuitry 50 maydetermine the highest amplitude sample in the search window to be theT-wave. In other examples, processing circuitry 50 may take apredetermined number of samples around a given sample and determine amedian, mean or mode and determine the T-wave to be the maximumamplitude median, mean or mode in the search window.

In some examples, processing circuitry 50 may determine a confidencelevel for the T-wave (146). In some examples, processing circuitry 50may not determine a confidence level for the T-wave. For example,processing circuitry 50 may determine a confidence level based on one ormore of a predetermined number of previous T-wave amplitudes or QTintervals. For example, if the amplitude of a detected T-wave is too lowcompared to previous T-waves or if the QT interval is very differentfrom the previous 12 QT intervals, then ICM 10 may give the detectedT-wave a low confidence level.

FIG. 18B is a continuation of FIG. 18A. Processing circuitry 50 maydetermine a QT interval based on the determined T-wave and thedetermined R-wave (148). For example, processing circuitry 50 maydetermine a time between an R-wave peak and the determined T-wave to bethe QT interval. In other examples, processing circuitry 50 maydetermine the QT interval to be a number of samples between the R-wavepeak and the determined T-wave. Processing circuitry 50 may alsodetermine a QTc interval (150). For example, processing circuitry 50 mayapply at least one of one of the Framingham formula, the Fridericaformula or the Bazett formula or another formula or technique to the QTinterval to determine the QTc interval.

Processing circuitry 50 may determine a trend of QT intervals or QTcintervals over time (152). For example, processing circuitry 50 maydetermine a mean, median, mode, standard deviation or any other trend ofdetermined QT intervals or QTc intervals over time.

FIG. 19 is a flow diagram depicting one example of how processingcircuitry 50 may determine whether to base the search window on theprevious RR interval or the current RR interval, e.g., as part ofdetermining the search window (138 of FIG. 18A). In the example of FIG.19, processing circuitry 50 may determine whether the difference betweenthe current RR interval and the previous RR interval is more than apredetermined amount (160). For example, processing circuitry 50 maydetermine whether the difference between the current RR interval and theprevious RR interval is more than a predetermined time period. Inanother example, processing circuitry 50 may determine whether thedifference between the current RR interval and the previous RR intervalis more than a predetermined number of samples larger. In some examples,if the difference between the current RR interval and the previous RRinterval is not more than a predetermined amount (the “NO” path in FIG.19), processing circuitry 50 may determine the search window based onthe previous RR interval (162). If the difference between the current RRinterval and the previous RR interval is more than a predeterminedamount (the “YES” path in FIG. 19), processing circuitry 50 maydetermine the search window based on the current RR interval (164). Insome examples, the predetermined amount may be 500 ms. In some examples,the predetermined amount may be 128 samples at 256 Hz.

FIG. 20 is a flow diagram depicting another example of how processingcircuitry 50 may determine whether to base the search window on theprevious RR interval or the current RR interval, e.g., as part ofdetermining the search window (138 of FIG. 18A). In the example of FIG.20, processing circuitry may determine if a previous beat was noisy(170). For example, ICM 10 may be configured to detect a noisy beat. Forexample, processing circuitry 50 may determine whether a beat is noisyby determining whether the current R wave is noisy or the ECG segmentbetween the current R wave and the next R wave is noisy or both. Forexample, processing circuitry may determine whether the current R waveis noisy by defining a window of a few samples before the R wave peakand a few samples after the R wave peak. Processing circuitry 50 maydetermine a noise count by counting the number of samples in the windowwhere there is a sign change of greater than a first threshold (e.g.,50) or a sign change of less than a second threshold (e.g., −50). Ifthis noise count is greater than or equal to a threshold (e.g., 5) thenprocessing circuitry 50 may determine the beat is noisy. Processingcircuitry 50 may determine whether the segment between the current Rwave and the next R wave is noisy in a similar manner. In this case,processor circuitry 50 may define a window starting from a few samplesafter the current R wave and ending a few samples before the next Rwave. Processing circuitry 50 may determine a noise count in a similarmanner although the thresholds may be different. If the noise count, isgreater than or equal to another threshold (e.g., 5 or some othercount), then processing circuitry may determine that the beat is noisy.

If processing circuitry 50 determines that the previous beat was notnoisy (the “NO” path in FIG. 20), processing circuitry 50 may determinethe search window based on the previous RR interval (172). If processingcircuitry 50 determines that the previous beat was noisy (the “YES” pathin FIG. 20), processing circuitry 50 may determine the search windowbased on the current interval.

While the techniques herein are described as being performed by variouselements, such as sensing circuitry 52 and processing circuitry 50, insome examples, other elements or a combination of elements may performthe techniques. For example, sensing circuitry 52 may perform techniquesdescribed as being performed by processing circuitry 50, processingcircuitry 50 may perform techniques described as being performed bysensing circuitry 52, or a combination of sensing circuitry 52 andprocessing circuitry 50 may perform techniques described as beingperformed by either.

The techniques described in this disclosure may be implemented, at leastin part, in hardware, software, firmware, or any combination thereof.For example, various aspects of the techniques may be implemented withinone or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalentintegrated or discrete logic QRS circuitry, as well as any combinationsof such components, embodied in external devices, such as physician orpatient programmers, stimulators, or other devices. The terms“processor,” “processing circuitry,” “controller” or “control module”may generally refer to any of the foregoing logic circuitry, alone or incombination with other logic circuitry, or any other equivalentcircuitry, and alone or in combination with other digital or analogcircuitry.

For aspects implemented in software, at least some of the functionalityascribed to the systems and devices described in this disclosure may beembodied as instructions on a non-transitory computer-readable storagemedium such as RAM, ROM, NVRAM, EEPROM, FLASH memory, magnetic media,optical media, or the like. The instructions may be executed to supportone or more aspects of the functionality described in this disclosure.

This disclosure includes the following non-limiting examples.

Example 1. A device comprising: one or more electrodes; sensingcircuitry configured to sense a cardiac signal via the one or moreelectrodes; and processing circuitry configured to: determine an R-waveof the cardiac signal; determine a previous RR interval of the cardiacsignal based on the determined R-wave; determine a current RR intervalof the cardiac signal based on the determined R-wave; determine a searchwindow based on one or more of the current RR interval or the previousRR interval; determine a T-wave of the cardiac signal in the searchwindow; and determine a QT interval based on the determined T-wave andthe determined R-wave.

Example 2. The device of example 1, wherein the processing circuitry isfurther configured to: determine a QTc interval.

Example 3. The device of example 2, wherein the processing circuitry isconfigured to determine the QTc interval by applying at least one of aFramingham formula, a Friderica formula, a Bazett formula, or otherformula or technique to the QT interval.

Example 4. The device of any combination of examples 1-3, wherein thesensing circuitry comprises a primary sensing channel and a secondarysensing channel.

Example 5. The device of example 4, wherein the primary sensing channelcomprises an auto adjusting threshold process.

Example 6. The device of example 5, wherein the auto adjusting thresholdprocess includes a blanking period.

Example 7. The device of any combination of examples 4-6, wherein thesecondary sensing channel comprises a fixed threshold process.

Example 8. The device of example 7, wherein the fixed threshold processincludes a blanking period.

Example 9. The device of example 8, wherein the blanking period of thefixed threshold process is longer than the blanking period of the autoadjusting threshold process.

Example 10. The device of any combination of examples 1-9, wherein thesensing circuitry comprises one or more band-pass filters.

Example 11. The device of example 10, wherein the one or more band-passfilters comprise a 10 Hz-32 Hz band-pass filter and a 6 Hz to 20 Hzband-pass filter.

Example 12. The device of any combination of examples 1-11, wherein thesensing circuitry comprises one or more rectifiers.

Example 13. The device of any combination of examples 1-12, wherein theprocessing circuitry is further configured to: determine whether adifference between the current RR interval and the previous RR intervalis more than a predetermined time period or more than a predeterminednumber of samples; and based on the difference between the current RRinterval and the previous RR interval being more than the predeterminedtime period or being more than a predetermined number of samples,determine the search window based on the current RR interval.

Example 14. The device of example 13, wherein the predetermined timeperiod is less than one second or the predetermined number of samples ismore than 100.

Example 15. The device of any of examples 1-14, wherein the processingcircuitry is further configured to: determine whether a previous beatwas noisy; and based on the previous beat being noisy, determine thesearch window based on the current RR interval.

Example 16. The device of any of examples 1-15, wherein the processingcircuitry is configured to determine the T-wave by determining a maximummedian value of samples in the search window.

Example 17. The device of any combination of examples 1-16, wherein theprocessing circuitry is further configured to: weight amplitudes ofsamples more heavily that are more likely to be located where the T-waveis located in the search window than samples that are less likely to belocated where the T-wave is located based on previous QT intervals orprevious QTc intervals.

Example 18. The device of any combination of examples 1-17, wherein theprocessing circuitry is further configured to: determine whether anamplitude of a sample in the search window is less than a threshold; andbased on the amplitude of the sample being less than the threshold,determine that the sample is not the T-wave.

Example 19. The device of any combination of examples 1-17, wherein theprocessing circuitry is further configured to: determine a confidencelevel of the T-wave based on one or more of a predetermined number ofprevious T-wave amplitudes, QT intervals, or QTc intervals.

Example 20. The device of example 19, wherein the predetermined numberof previous T-wave amplitudes, QT intervals, or QTc intervals is 12.

Example 21. The device of any combination of examples 1-20, wherein theprocessing circuitry is further configured to: determine a trend ofdetermined QT intervals or QTc intervals over time.

Example 22. The device of example 21, wherein the trend is one or moreof a mean, median, mode or standard deviation of determined QT intervalsor QTc intervals.

Example 23. The device of any combination of examples 1-22, wherein theprocessing circuitry is further configured to: determine a time or acount of which QT intervals or QTc intervals are longer than apredetermined threshold.

Example 24. A method comprising: sensing a cardiac signal; determiningan R-wave of the cardiac signal; determining a previous RR interval ofthe cardiac signal based on the determined R-wave; determining a currentRR interval of the cardiac signal based on the determined R-wave;determining a search window based on one or more of the current RRinterval or the previous RR interval; determining a T-wave of thecardiac signal in the search window; and determining a QT interval basedon the determined T-wave and the determined R-wave.

Example 25. The method of example 24, further comprising: determining aQTc interval.

Example 26. The method of example 25, wherein determining the QTcinterval comprises applying at least one of a Framingham formula, aFriderica formula, a Bazett formula, or other formula or technique tothe QT interval.

Example 27. The method of any combination of examples 24-26, wherein thesensing the cardiac signal comprises sensing with a primary sensingchannel and a secondary sensing channel.

Example 28. The method of example 27, wherein the primary sensingchannel comprises an auto adjusting threshold process.

Example 29. The method of example 28, wherein the auto adjustingthreshold process includes a blanking period.

Example 30. The method of any combination of examples 27-29, wherein thesecondary sensing channel comprises a fixed threshold process.

Example 31. The method of example 30, wherein the fixed thresholdprocess includes a blanking period.

Example 32. The method of example 31, wherein the blanking period of thefixed threshold process is longer than the blanking period of the autoadjusting threshold process.

Example 33. The method of any combination of examples 24-32, wherein thesensing comprises applying one or more band-pass filters to a sensedsignal.

Example 34. The method of example 33, wherein the one or more band-passfilters comprise a 10 Hz-32 Hz band-pass filter and a 6 Hz to 20 Hzband-pass filter.

Example 35. The method of any combination of examples 33-34, furthercomprising applying a rectifier to a band-passed signal.

Example 36. The method of any combination of examples 24-35, furthercomprising: determining whether a difference between the current RRinterval and the previous RR interval is more than a predetermined timeperiod or more than a predetermined number of samples; and based on thedifference between the current RR interval and the previous RR intervalbeing more than the predetermined time period or being more than apredetermined number of samples, determining the search window based onthe current RR interval.

Example 37. The method of example 36, wherein the predetermined timeperiod is less than one second or the predetermined number of samples ismore than 100.

Example 38. The method of any of examples 24-37, further comprising:determining whether a previous beat was noisy; and based on the previousbeat being noisy, determining the search window based on the current RRinterval.

Example 39. The method of any of examples 24-38, wherein determining theT-wave comprises determining a maximum median value of samples in thesearch window.

Example 40. The method of any combination of examples 24-39, furthercomprising: weighting amplitudes of samples more heavily that are morelikely to be located where the T-wave is located in the search windowthan samples that are less likely to be located where the T-wave islocated based on previous QT intervals or QTc intervals.

Example 41. The method of any combination of examples 24-40, furthercomprising: determining whether an amplitude of a sample in the searchwindow is less than a threshold; and based on the amplitude of thesample being less than the threshold, determining that the sample is notthe T-wave.

Example 42. The method of any combination of examples 24-41, furthercomprising: determining a confidence level of the T-wave based on one ormore of a predetermined number of previous T-wave amplitudes, QTintervals or QTc intervals.

Example 43. The method of example 42, wherein the predetermined numberof previous T-wave amplitudes, QT intervals or QTc intervals is 12.

Example 44. The method of any combination of examples 24-43, furthercomprising: determining a trend of determined QT intervals or QTcintervals over time.

Example 45. The method of example 44, wherein the trend is one or moreof a mean, median or mode of determined QT intervals or QTc intervals.

Example 46. The method of any combination of examples 24-45, furthercomprising: determine a time or a count of which QT intervals or QTcintervals are longer than a predetermined threshold.

Example 47. A non-transitory, computer-readable storage medium storing aset of instructions that, when executed, cause a system to: determine anR-wave of the cardiac signal; determine a previous RR interval of thecardiac signal based on the determined R-wave; determine a current RRinterval of the cardiac signal based on the determined R-wave; determinea search window based on one or more of the current RR interval or theprevious RR interval; determine a T-wave of the cardiac signal in thesearch window; and determine a QT interval based on the determinedT-wave and the determined R-wave.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A device comprising: one or more electrodes;sensing circuitry configured to sense a cardiac signal via the one ormore electrodes; and processing circuitry configured to: determine anR-wave of the cardiac signal; determine a previous RR interval of thecardiac signal based on the determined R-wave; determine a current RRinterval of the cardiac signal based on the determined R-wave; determinea search window based on one or more of the current RR interval or theprevious RR interval; determine a T-wave of the cardiac signal in thesearch window; and determine a QT interval based on the determinedT-wave and the determined R-wave.
 2. The device of claim 1, wherein theprocessing circuitry is further configured to: determine a QTc interval.3. The device of claim 2, wherein the processing circuitry is configuredto determine the QTc interval by applying at least one of a Framinghamformula, a Friderica formula, a Bazett formula, or other formula ortechnique to the QT interval.
 4. The device of claim 1, wherein theprocessing circuitry is further configured to: determine whether adifference between the current RR interval and the previous RR intervalis more than a predetermined time period or more than a predeterminednumber of samples; and based on the difference between the current RRinterval and the previous RR interval being more than the predeterminedtime period or being more than a predetermined number of samples,determine the search window based on the current RR interval.
 5. Thedevice of claim 1, wherein the processing circuitry is furtherconfigured to: determine whether a previous beat was noisy; and based onthe previous beat being noisy, determine the search window based on thecurrent RR interval.
 6. The device of claim 1, wherein the processingcircuitry is configured to determine the T-wave by determining a maximummedian value of samples in the search window.
 7. The device of claim 1,wherein the processing circuitry is further configured to: weightamplitudes of samples more heavily that are more likely to be locatedwhere the T-wave is located in the search window than samples that areless likely to be located where the T-wave is located based on previousQT intervals or previous QTc intervals.
 8. The device of claim 1,wherein the processing circuitry is further configured to: determinewhether an amplitude of a sample in the search window is less than athreshold; and based on the amplitude of the sample being less than thethreshold, determine that the sample is not the T-wave.
 9. The device ofclaim 1, wherein the processing circuitry is further configured to:determine a confidence level of the T-wave based on one or more of apredetermined number of previous T-wave amplitudes, QT intervals, or QTcintervals.
 10. The device of claim 1, wherein the processing circuitryis further configured to: determine a time or a count of which QTintervals or QTc intervals are longer than a predetermined threshold.11. A method comprising: sensing a cardiac signal; determining an R-waveof the cardiac signal; determining a previous RR interval of the cardiacsignal based on the determined R-wave; determining a current RR intervalof the cardiac signal based on the determined R-wave; determining asearch window based on one or more of the current RR interval or theprevious RR interval; determining a T-wave of the cardiac signal in thesearch window; and determining a QT interval based on the determinedT-wave and the determined R-wave.
 12. The method of claim 11, furthercomprising: determining a QTc interval.
 13. The method of claim 12,wherein determining the QTc interval comprises applying at least one ofa Framingham formula, a Friderica formula, a Bazett formula, or otherformula or technique to the QT interval.
 14. The method of claim 11,further comprising: determining whether a difference between the currentRR interval and the previous RR interval is more than a predeterminedtime period or more than a predetermined number of samples; and based onthe difference between the current RR interval and the previous RRinterval being more than the predetermined time period or being morethan a predetermined number of samples, determining the search windowbased on the current RR interval.
 15. The method of claim 1, furthercomprising: determining whether a previous beat was noisy; and based onthe previous beat being noisy, determining the search window based onthe current RR interval.
 16. The method of claim 11, wherein determiningthe T-wave comprises determining a maximum median value of samples inthe search window.
 17. The method of claim 11, further comprising:weighting amplitudes of samples more heavily that are more likely to belocated where the T-wave is located in the search window than samplesthat are less likely to be located where the T-wave is located based onprevious QT intervals or QTc intervals.
 18. The method of claim 11,further comprising: determining whether an amplitude of a sample in thesearch window is less than a threshold; and based on the amplitude ofthe sample being less than the threshold, determining that the sample isnot the T-wave.
 19. The method of claim 11, further comprising:determining a confidence level of the T-wave based on one or more of apredetermined number of previous T-wave amplitudes, QT intervals or QTcintervals.
 20. A non-transitory, computer-readable storage mediumstoring a set of instructions that, when executed, cause a system to:determine an R-wave of the cardiac signal; determine a previous RRinterval of the cardiac signal based on the determined R-wave; determinea current RR interval of the cardiac signal based on the determinedR-wave; determine a search window based on one or more of the current RRinterval or the previous RR interval; determine a T-wave of the cardiacsignal in the search window; and determine a QT interval based on thedetermined T-wave and the determined R-wave.